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Fast Time-of-Flight Phase Unwrapping and Scene Segmentation Using Data Driven Scene Priors

<p> This thesis regards the method of full field time-of-flight depth imaging by way of amplitude modulated continuous wave signals correlated with step-shifted reference waveforms using a specialized solid state CMOS sensor, referred to as photonic mixing device. The specific focus deals with the inherent issue of depth ambiguity due to a fundamental property of periodic signals: that they repeat, or wrap, after each period, and any signal shifted by a whole number of wavelengths is indistinguishable from the original. Recovering the full extent of the signal&rsquo;s path is known as phase unwrapping. The common, accepted solution requires the imaging of a series of two or more signals with differing modulation frequencies to resolve the ambiguity, the time delay of which will result in erroneous or invalid measurements for non-static elements of the scene. This work details a physical model of the observable illumination of the scene which provides priors for a novel probabilistic framework to recover the scene geometry by imaging only a single modulated signal. It is demonstrated that this process is able to provide more than adequate results in a majority of representative scenes, and that it can be accomplished on typical computer hardware at a speed that allows for the range imaging to be utilized in real-time, interactive applications.</p><p> One such real-time application is presented: alpha-matting, or foreground segmentation, for background substitution of live video. This is a generalized version of the common technique of green-screening that is utilized, for example, by every local weather reporter. The presented method, however, requires no special background, and is able to perform on high resolution video from a lower resolution depth image.</p>

Identiferoai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3746704
Date16 January 2016
CreatorsCrabb, Ryan Eugene
PublisherUniversity of California, Santa Cruz
Source SetsProQuest.com
LanguageEnglish
Detected LanguageEnglish
Typethesis

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